Method for segmenting and identifying a document, in particular a technical chart

ABSTRACT

The object of the invention is a method of processing information contained in an image, including:
         a first processing ( 1 - 4 ), for defining an area of interest of the image ( 8 ),   effecting an adaptive thresholding ( 1 - 8 ) of this area of interest in order to obtain a threshold image ( 10 ) of this area of interest, referred to as the first thresholded image,   the segmentation ( 1 - 12 ) of the thresholded image, in order to obtain a first set of morphological layers ( 14 - 1, 14 - 2, 14 - 3, . . .  ) of the thresholded image.

TECHNICAL FIELD AND PRIOR ART

The invention described relates to a method of processing informationcontained in an image and a method of recognising shapes. It finds anapplication in the digital processing of an image, in particular in thefield of the recognition of documents, for example technical documents.

The recognition of documents and the recovery of data on the paperdocuments are currently of great importance. It is therefore necessaryto develop strategies for the electronic management of documentation,with a view to recovering the information appearing thereon, possiblyincluding acquiring documents existing on a paper medium or the like(tracings, microfiches, etc).

At the present time a certain number of techniques for convertingdrawings, notably technical drawings, are known, making it possible tosupply company document databases.

Where the amount of documentation to be converted is high, and using amanual technique, the workload proves redhibitory. It is then in fact acase of capturing the textual and graphical information of the documentusing digitising tables. In addition, the quality of the data thusgenerated is not guaranteed. Such a conversion also often requiressubcontracting, and poses the problem of the confidentiality of the datain a competition context.

Tools for the automatic or semi-automatic conversion of technicaldrawings are also known. These tools, and the corresponding techniques,have parameterising which is tricky, complex and very sensitive tovariations in the document representation rules. If parasitic signalsare present in the document, these tools progressively lose theireffectiveness and require increasing manual processing.

Finally, techniques for the digital processing of images coded with adepth of 1 bit and of monochrome documents are known. Though thesetechniques allow a slight “cleaning” of the document, they do not makeit possible to separate the useful information from the noise when thelatter is preponderant. This is because a technical document is oftenthe result of the superimposition of an essential document and dressingwhich is purely indicative for a given theme.

Moreover, the traditional tools for shape recognition, in particular inthe case of long elements, often require the introduction of data by anoperator, for example those concerning segments or their length. Thesetools are therefore not automated; and in addition this selection byhand does not account for the primitives used.

In addition, these traditional shape recognition tools have a complexman/machine interface, requiring on the part of the operator a learningwhich is not commensurate with his job. If he is, for example, aspecialist on electrical distribution documentation, he will have toacquire competencies in the field of image processing.

Finally, these tools require significant manual corrections after shaperecognition.

DISCLOSURE OF THE INVENTION

With respect to these techniques, the object of the invention is amethod of processing an image, or information contained in an image,including:

-   -   a first processing, for defining an area of interest of the        image,    -   the effecting of an adaptive thresholding of this area of        interest in order to obtain a thresholded image of this area of        interest, referred to as a first thresholded image,    -   the segmentation of the thresholded image, in order to obtain a        first set of morphological layers of the thresholded image.

Morphological layer means a set of shapes having similar geometriccharacteristics (for example size, surfaces, internal or externalperimeters, length to width ratio, etc). This similar character can bedefined, for example in the case of a surface, by a density of pixelsper surface element.

Effecting a first processing for defining an area of interest of theimage, and then effecting processing by thresholding, improves thissecond step in an obvious manner.

The first processing can be carried out itself by thresholding (ormultithresholding), for example by applying the OTSU orKITTLER-ILLINGWORTH algorithm.

A shape recognition processing can then be applied to each of themorphological layers of the first set of morphological layers. It isthus possible to choose, for each layer, the most suited recognitionprocessing.

Before effecting a shape recognition processing, the method according tothe invention allows a first analysis and a separation of themorphological layers from the very start, on the initial image (whichwill usually be a “raster” image, i.e. an image obtained by means of ascanner of a real image). Thus, according to requirements, there is theadvantage, from the very first processing steps, of a separation of themorphological layers.

Because of a morphological classification of the object, affording ahigh degree of appropriateness of algorithms dedicated to the shapes tobe recognised, this method guarantees automation of the processings,associated with reduced parameterising. The result of these processingsis a highly structured vectorial document whose natural topology isrespected.

Moreover, such a method can be adapted without modification of theparameterising to the variations in representation of the thematics, ormorphological layers. The invention makes it possible to identify thesedifferent layers, to separate them, and to keep only the layers whichare useful for the shape recognition processing.

Finally, the invention is well adapted to the recognition of networkdrawings (for example telecommunication networks, or water, gas orelectricity distribution networks), even if they contain backgroundmaps.

The first processing can be followed by a step of refining the area ofinterest of the image, for example by expansion or erosion, in order torefine the pixel population selection.

This first processing (spatial mask), as well as moreover the first stepof classification into different morphological layers of the image, makeit possible to separate the “useful” information in a document from theinformation judged to be purely indicative.

It is possible, after segmentation of the thresholded image, to effect astep of thresholding the parts of the thresholded image corresponding toone of the morphological layers, the image obtained being referred to asthe second thresholded image.

The information consisting of the grey levels of the thresholded image,but limited by, or to, one of the morphological layers, which thereforeis then itself used as a mask on the thresholded image, is thereforerepeated. This information then itself undergoes a thresholdingprocessing, which next makes it possible to improve the segmentation,the latter in its turn making it possible to obtain a second set ofmorphological layers.

A shape recognition processing can next be applied to each of the layersof the second set of morphological layers.

The invention also relates to a device for implementing an imageprocessing method as described above.

The invention therefore relates to a device for processing theinformation contained in an image, having:

-   -   means for effecting a first processing, making it possible to        define an area of interest of the image,    -   means for effecting an adaptive thresholding of the said area of        interest, and for obtaining a thresholded image,    -   means of segmenting the thresholded image, in order to obtain a        first set of morphological layers of the thresholded image.

In addition means can be provided for applying a shape recognitionprocessing to each of the morphological layers of the first set ofmorphological layers.

This device can also have means for effecting, after segmentation of thethresholded image, a step of thresholding the parts of the thresholdedimage corresponding to one of the morphological layers, the imageobtained being said to be the second thresholded image.

Another object of the invention is a method of recognising shape in animage, including:

-   -   a skeletonisation of the image, in order to establish a skeleton        of the elements of the image,    -   a polygonalisation using the pixels of the skeleton of the        image, in order to generate segments or bipoints,    -   a structuring of the bipoints, in order to collect together        those belonging to the same, shape in the image.

The image to which the shape recognition method is applied can be drawnfrom one of the sets of morphological layers defined above.

The skeletonisation of the image can include:

-   -   a search for the degree of interiority of each pixel,    -   a search for the pixels with the highest degree of interiority.

The polygonalisation step can be followed by a processing fordetermining the shapes to be recognised at the level of the multiplepoints. This processing can include the use of first and secondskeleton-tracking algorithms:

-   -   the first algorithm effecting a line tracking favouring        bifurcation to the left in the case of a multiple node,        generating a first skeleton tracking,    -   the second algorithm effecting a line tracking favouring a        bifurcation to the right in the case of a multiple node,        generating a second skeleton tracking.

A step of merging the data resulting from the application of the twoskeleton-tracking algorithms can also be provided, in order to eliminatethe redundant information contained in the two skeleton trackings. Thismerging of the data can include for example the determination of thesegments, or bipoints, of one of the skeleton trackings, which areincluded, partially or totally, in the other.

The structuring of the bipoints can include the following steps:

-   -   a) establishing a single list of bipoints, in increasing order        of length,    -   b) selecting the largest of the bipoints in this last list,    -   c) seeking partial inclusion with the other bipoints,    -   d) testing by polygonalisation, when a partially included        bipoint is found during the previous step,    -   e) if the result of step d) is positive, erasing the bipoints,        and replacing by the merged bipoint, and returning to c),    -   f) continuing step c), if the result of d) includes more than        two points,    -   g) if step d) supplies no more new bipoints, storing the last        bipoint issuing from step d), erasing this bipoint from the list        of bipoints established at a), and returning to a).

According to another embodiment, the shape recognition method accordingto the invention also includes a step for assembling the contiguousbipoints in the same segment, the assembling being effected by seeking,step by step, physical continuity, in the very close vicinity of eachpoint of a bipoint to be extended by continuity.

Where the image to which the shape recognition method applies representstechnical premises or chambers situated at ends of sections or arcs, theshape recognition method can also include a step of seeking occlusionsin the image, a step of filtering the occlusions and a step of seekingthe number of ends of sections situated in the vicinity where a chamberwas detected.

Finally, another object of the invention is a device for implementing ashape recognition method according to the invention, as described above.

Such a device has;

-   -   means for effecting a skeletonisation of the image, in order to        establish a skeleton of the image,    -   means for effecting a polygonalisation using the pixels of the        skeleton of the image,    -   means for structuring the bipoints and collecting together those        belonging to the same shape in the image.

The device can also have means for executing first and secondskeleton-tracking algorithms:

-   -   the first algorithm effecting a line tracking favouring        bifurcation to the left in the case of a multiple node,        generating a first skeleton tracking,    -   the second algorithm effecting a line tracking favouring a        bifurcation to the right in the case of a multiple node,        generating a second skeleton tracking.

The means for executing first and second skeleton-tracking algorithmscan also make it possible to merge the data resulting from the executionof the two skeleton-tracking algorithms, in order to eliminate theredundant information containing the two skeleton trackings.

This device can also have the means for collecting together thecontiguous bipoints in one and the same segment.

BRIEF DESCRIPTION OF THE FIGURES

In any event, the characteristics and advantages of the invention willemerge more clearly in the light of the following description. Thisdescription relates to the example embodiments given for explanation andnon-limitatively, with reference to the accompanying drawings, in which:

FIGS. 1A and 1B depict schematically the steps of a method according tothe invention.

FIG. 2 is an example of a document to be recognised.

FIG. 3 is a histogram of the document of FIG. 2.

FIG. 4 depicts the image obtained, after masking of the document.

FIG. 5 is a histogram of the image depicted in FIG. 4.

FIG. 6 depicts the image obtained, after adaptive thresholding of theimage depicted in FIG. 4.

FIGS. 7A to 7C depict three morphological layers obtained bysegmentation of the image depicted in FIG. 6.

FIG. 8 depicts a device for implementing the present invention.

FIG. 9 is an example of a result of polygonalisation.

FIGS. 10A and 10B are examples of a result of a skeleton-trackingalgorithm, respectively trigonometric and anti-trigonometric.

FIG. 11 illustrates the principle of inclusion.

FIG. 12 depicts possible configurations between bipoints.

FIG. 13 depicts a network outline, after final merging of the bipoints,

FIG. 14 depicts an outline obtained by structuring of the bipoints.

FIG. 15 is an example of chambers on a scanned original drawing.

FIG. 16 depicts an example of occlusion.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

FIGS. 1A and 1B depict steps of a method which can be implemented inaccordance with the invention.

A technical document 2 (FIG. 1A) is first of all “scanned” (or sampled,steps 1-2), for example in an 8-bit format, and at high resolution, forexample greater than 400 dpi. This step therefore supplies a sourceimage 4.

To this source image, a first processing 1-4 will be applied, fordefining an area of interest of the image. This first step (alsoreferred to as a global approach step) can be implemented, for example,by thresholding, the threshold level or levels applied being determinedby a thresholding algorithm from the histogram of the grey level of thesource image. This thresholding makes it possible to define differentclasses (at least two) of grey levels in the histogram of the image. Forexample, at least one of these classes corresponds to the background ofthe image and is not retained: the mode of the background is thereforereduced to 0 in the source image. From this operation, a mask 6 cantherefore be derived, which makes it possible to define an area ofinterest of the image.

The masked image 8 is obtained by applying (1-6) the mask to the sourceimage 4. This masked image therefore no longer contains any more thanthe elements of the document which are chosen as being significant.

A local processing (local approximation) of the image is next performed:the masked image will undergo an adaptive thresholding (step 1-8) usinga thresholding algorithm. A so-called “thresholded” image 10 is thusobtained. The thresholding operation performed on the masked image ismuch more effective, or much more precise, than the one which would havebeen performed directly on the source image 4. In particular, it ispossible to obtain, by virtue of this method, the differentiation ofcertain details of the image, which would have been merged ifthresholding had been carried out directly on the source image.

An operation of extracting the masses of related pixels is then carriedout (FIG. 1B, step 1-10). In this way images 12 are produced, each imagerepresenting similar masses, or shapes, of pixels.

These shapes are next sorted (step 1-12) by morphological criteria. Theoperation consisting of “labelling” the shapes and classifying them inaccordance with morphological criteria is referred to as a segmentationoperation. In this way the definition of physical “layers” 14-1, 14-2,14-3, of the document is obtained. Sorting the shapes according to theirmorphology next makes it possible to subject them to dedicatedrecognition algorithms, adapted to each of the shapes.

Each of the different physical layers thus defined can be considered inits turn as a mask for the source image 4. Applying these masks to thesource image makes it possible to obtain small images 16-1, 16-2, 16-3,. . . to the format of the source image (here B bits). Each physicallayer therefore makes it possible to find the information whichcorresponds to it, in terms of grey level, in the source image.

Each of the images 16-1, 16-2, 16-3, . . . can in its turn be subjectedto processing by thresholding, in order to improve the segmentation ofthe image. Once the new thresholds have been determined on one of theimages 16-i, a new cycle can be recommenced with this image 16-i: thisimage can be resegmented by the size of the closely related masks. It isthus possible to separate the shapes (or characters) which, on thetechnical drawing 2, were disconnected or separate, but which appearedto be connected in the source image 4.

FIG. 2 is a characteristic example of the type of document to berecognised. The elements to be extracted have been marked, those whichderive the route of a telephone network. Thus there are found characters20, network sections 22, chambers 24 (the cable chambers), a buildingterminal 26, the concentration points (CP) 28 (boxes enabling a user tobe connected to a network) or edges of plots 27. An examination of thisimage in terms of grey level shows a strong background noise, due to thequality of the medium and the high degree of connectedness of theelements to be extracted with the background of the cartographicdrawing. The description of the histogram of this image, depicted inFIG. 3, shows these characteristics.

This histogram includes essentially three regions:

-   -   a first region 30 (or “high mode”), which contains solely the        background of the image,    -   a central region 32, which contains mainly cadastral        information,    -   a third region 34 (or “low mode”), which contains mainly the        information relating to the telephone network.

In a first approximation of the image, referred to as the globalapproximation, the area of interest encompassing the network isdetermined. For this purpose, an adaptive multithresholding is carriedout; this is a case of multithresholding in three classes, based on theOTSU algorithm. The vertical axes indicate the threshold values S₁, S₂calculated by the algorithm from the histogram.

A colour can be allocated to the pixels of each of the three classes,and the spatial distribution of these classes (FIG. 4) can bedetermined.

It will be observed that the network fits entirely within the classdepicted in grey in the image. However, the noise remains very great andit is not possible to present this class directly to a recognitionsystem. On the other hand, this class corresponds to the spatial extentof the initial image containing the network.

By virtue of the global approximation of the image, an area of interestcorresponding to the spatial extent in which the network entirely fitshas therefore been isolated. This area is the result of amultithresholding based on seeking the characteristic modes of thehistogram. Starting from the principle that the network is included inthis class, a masking of the initial image is effected in order toeliminate the pixels of the background. It is by selecting the classesthat the spatial mask is selected.

The image obtained is a “division” of the image into grey levels g whosehistogram is given in FIG. 5. The discrimination of the modes isappreciably improved therein, because of the exclusion of the populationof the background pixels on the image. As from now, the processing chainno longer uses any more than the significant elements of the document.

Several thresholding algorithms have been used (OTSU,KITTLER-ILLINGWORTH and a spectral classifier, ISODATA, described in thesoftware IMAGINE from ERDAS). The results obtained have shown that thereturn to the source image, in order to use only a significantpopulation of pixels, clearly optimises the functioning of the differentthresholding processings.

In addition, in the constitution of the mask, it is possible to useconventional mathematical morphology operators, which are easier toproduce (expansion or erosion of images), in order to refine thepopulation selection.

Finally, an area of interest has been constructed on which asegmentation of the shapes of the image (FIG. 6) can be carried out.

It can be seen in this figure that the majority of the information ofthe cartographic background has been eliminated; this is the case,notably, with the hatching of buildings, which are strong disturbingelements in vectorisation. This information is not necessarily lostsince it is discriminated by the choice of classes in the thresholdedimage.

This thresholded image will next be segmented.

The segmentation operation consists of labelling the shapes present, andclassifying them according to morphological criteria. Each of thelabelled shapes constitutes a related mass of pixels. In our prototype,this segmentation consists of determining three classes of shapes:

-   -   three major linear elements, containing mainly the conduits        (main cable sections in the network) and the cable chambers        (FIG. 7A),    -   shapes corresponding to the terminal equipment of the network        (CP, whose symbology is here represented by triangles or        rectangles) and to the characters (FIG. 7B),    -   shapes whose morphology does not come within any of the above        criteria (FIG. 7C).

These three layers constitute a physical model of the document. Sortingthe shapes according to their morphology will make it possible tosubject them to dedicated recognition algorithms adapted to each of theshapes.

Each layer contains the majority of the elements to be recognised.Hereinafter, attention will be paid, through the recognition of shapes(see below), to studying the layers, notably those containing thesections of main cables (FIG. 7A).

The three layers can be considered to be a set of masks on objects ofthe same class. It is therefore possible, using these masks, to find theinformation in terms of grey levels concerning a single class. Thegrey-level image generated from different masks can thus berethresholded, in order to improve the segmentation of the image. Thisis because, by reducing the number of types of information in the image,the determination of automatic thresholds will be more pertinent.

Once the new thresholds have been determined, a new cycle recommenceswith this new image. This new image is resegmented by the size of theclosely related masses.

The results are entirely satisfactory. The majority of the charactersconnected have been disconnected. The only ones which still remainconnected are also connected on the original image. They can thereforenot be segmented at this level of processing. Finally, each of thelayers thus obtained is presented to the shape recognition process byallowing the use of algorithms which are best suited to the physicalthematics proposed.

The thresholding algorithms used can for example implement the OTSUmethod, or that of KITTLER-ILLINGWORTH.

The OTSU method (“A threshold selection method from grey levelhystograms”, IEEE Trans. Syst. Man Cyber, 1, p. 62-66, 1979) assimilatesthe problem of determining a pertinent threshold T, for the binarisationof the image, to a problem of better classification of the pixels intotwo subgroups C_(1T) and C_(2T).

One of these classes always contains the background, and the other theobjects of the image.

The histogram of the image, for a grey level t, makes it possible tocalculate the statistical data seen previously: a priori probability ofbelonging, mean grey level, and variance for each class C_(1 and C) ₂.OTSU deduced therefrom the following equations:

-   -   mean grey level for the entire image:        $g_{T} = {{\sum\limits_{g = 0}^{L}\quad{{g \cdot p}\quad(g)}} = {\sum\limits_{j = 1.2}^{\quad}\quad{\omega\quad j\quad{(t) \cdot {gj}}\quad(t)}}}$    -   variance for the entire image:        ${\sigma^{2}\quad(t)} = {{\sum\limits_{g = 0}^{L}\quad{{\left( {g - g_{T}} \right)^{2} \cdot p}\quad(g)}} = {{\sigma^{2}\quad w\quad(t)} + {\sigma^{2}\quad B\quad(t)}}}$        with:        ${\sigma^{2}\quad w\quad(t)} = {\sum\limits_{j = 1.2}^{\quad}\quad{\omega\quad j\quad{(t) \cdot \sigma^{2}}\quad j\quad(t)}}$        referred to as intra-class variance,        ${\sigma^{2}\quad B\quad(t)} = {\sum\limits_{j = 1.2}^{\quad}\quad{\omega\quad j\quad{(t) \cdot \left( {{{gj}\quad(t)} - g_{T}} \right)^{2}}}}$        referred to as inter-class variance.

OTSU introduces the following discrimination criterion, dependent on t,which will have to be maximised:${\eta\quad(t)} = \frac{\sigma^{2}\quad B\quad(t)}{\sigma^{2}\quad T}$

This ratio represents the pertinence of the choice of threshold t forbinarising the image. This is because, whatever the total variance ofthe image, an optimum threshold results in a maximum value of thevariance between the class corresponding to the background and thatcorresponding to the objects σ²B(t).

Therefore, if t is optimum, and σ, the variance for the entire image,does not depend on t, □(t) reaches its maximum. The evaluation of □(t)requires the prior calculation of a σ²B(t) and of σ²T. Instead of using□(t) for seeking the optimum threshold, it is possible to use the factthat: σ²T=σ² _(w)(t)+σ² _(B)(t) is a constant for any t.

However, for T, optimum threshold, there is a maximum σ²B(t), i.e. aminimum σ² _(w)(t).

Consequently, classifying according to this first IU method amounts tofinding the boundary which on the one hand maximises the inter-classvariance so as to is separate the classes and on the other handminimises the intra-class variance so as to group together the greylevels of each class around its mean.

In the method of KITTLER and ILLINGWORTH (Kittler et al., “Minimum errorthresholding”, Pattern Recognition, 25(9), p. 963-973, 1992), theinitial hypothesis is that the populations C₁ and C₂ associated with thebackground and object follow Gaussian distributions.

Let T be the Gaussian model change threshold given a priori, and leth(g) be the histogram of the image; then it is possible to define theparameters of each population Ci(I=1.2):□i(T), gi(T) and σ²i(T)

Let h(g/i,T) be the approximate law of h(g), conditionally upon thepopulation i and the threshold T. For a grey level g in [0, L], theconditional probability is defined for g being replaced in the image bya correct value, after binarisation, when T is chosen by:${e\quad\left( {g,T} \right)} = {{\frac{h\quad{\left( {{g/i},T} \right) \cdot \omega}\quad i\quad(T)}{h\quad(g)}\quad{where}\quad i} = \left\{ \begin{matrix}{{1\quad{if}\quad g} \leq T} \\{{2\quad{if}\quad g} > T}\end{matrix} \right.}$Noting:ε(g, T)=−2 log(h(g).e(g, T))=−2 log(h, g/i,T.□i(T))  (4)

However, by hypothesis: $\begin{matrix}{{h\quad\left( {{g/i},T} \right)} = {{\frac{1}{\sqrt{2\quad\pi\quad\sigma\quad i\quad(T)}}\quad\exp\quad\left( {- \left\lbrack \frac{\left( {x - {{gi}\quad(T)}} \right)^{2}}{2\quad\sigma^{2}\quad i\quad(T)} \right\rbrack} \right)\quad{for}\quad i} = 1.2}} & (5)\end{matrix}$

Combining (4) and (5), there is deduced: $\begin{matrix}{{ɛ\quad\left( {g,T} \right)} = {{2\quad\log\quad\sqrt{2\quad\pi}} + {2\quad\log\quad\sigma\quad i\quad(T)} + \left\lbrack \frac{g - {{gi}\quad(T)}}{\sigma\quad i\quad(T)} \right\rbrack^{2} - {2\quad\log\quad\omega\quad i\quad(T)}}} & (6)\end{matrix}$

The only concern is with the non-constant part, given by:ε′(g, T)=ε(g, T)−2 log √{square root over (2π)}  (7)ε′(g, T) is an indicator of the correct classification of g. The smallerit is, the better will be T for the classification of this pixel.

In order to evaluate the thresholding quality obtained for a given valueof T, KITTLER & ILLINGWORTH define the following criterion:$\begin{matrix}{{J\quad(T)} = {\sum\limits_{g}^{\quad}\quad{h\quad{(g) \cdot ɛ^{\prime}}\quad\left( {g,T} \right)}}} & (8)\end{matrix}$

The optimum threshold T* for the binarisation of the image will be givenby: J(T*)=min_(T)J(T).

There is also obtained: $\begin{matrix}{{J\quad(T)} = {{\sum\limits_{g = 0}^{T}\quad{h\quad{(g) \cdot \left\lbrack {{2\quad\log\quad\sigma_{1}\quad(T)} - {2\quad\log\quad\omega_{1}\quad(T)} + \left( \frac{g - {g_{1}\quad(T)}}{\sigma_{1}\quad(T)} \right)^{2}} \right\rbrack}}} + {\sum\limits_{g = {T + 1}}^{L}\quad{h\quad{(g) \cdot \left\lbrack {{2\quad\log\quad\sigma_{2}\quad(T)} - {2\quad\log\quad\omega_{2}\quad(T)} + \left( \frac{g - {g_{2}\quad(T)}}{\sigma_{2}\quad(T)} \right)^{2}} \right\rbrack}}}}} & (9)\end{matrix}$

Starting from (9) and considering (1), (2) and (5), the followingformulation of the KITTLER & ILLINGWORTH criterion is arrived at:J(T)=1+2[ω₁(T)log σ₁(T)+ω₂(T)log σ₂(T)−2[ω₁(T)log ω₁(T)+ω₂(T)logω₂(T)]  (10)

In order to determine the optimum threshold, it suffices to seek T suchthat J(T) is minimum.

These methods can be extended to multithresholding. This is of interestin certain cases, in particular in the one disclosed above. This isbecause the histograms given as an example have two modes, and a broadundefined region between the two. It therefore appears legitimate towish to seek three classes, i.e. one for each mode, and a last one forthe undefined region, whence the necessity to find two thresholds. Thisis parameter chosen a priori. Choosing more than two thresholds couldalso be advantageous in certain cases.

In the method described above, a technical document is first of allscanned by means of a suitable device of the scanner type. Originalimages are thus obtained, which can be stored in the form of digitalimages.

The image processing method according to the invention can beimplemented by means of a Unix or Windows workstation. This station canwork independently of the scanner whose images were previously stored.The data processing program according to the invention can be stored onmagnetic tapes or on one or more diskettes. The programs are developedunder Unix in ANSI-compatible C language and can be carried on differentUNIX/Motif workstations or a microcomputer of the PC type under WindowsNT4. The workstation can also incorporate a display device.

The computer system used has a calculation section with a microprocessorand all the electronic components necessary for processing the images.

FIG. 8 is a simplified representation, in block form, of one of thecomponents of the computer used. A microprocessor 39 is connected, by abus 40, to RAM memories 41 for storing the data and programinstructions, and an ROM memory 42 for storing the instructions of theprocessing program produced. A computer having these elements can alsohave other peripheral elements such as the scanner or display devicementioned above or a mouse, modem, etc. Data on images to be processedor on programs to be applied can be transferred to the RAM memory 41from storage or memory media such as disk, CD ROM, magneto-opticaldisks, hard disks, etc. A keyboard 38 makes it possible to enterinstructions into the device.

In general terms an apparatus or device according to the invention, forprocessing information contained in an image, has:

-   -   storage means for storing instructions for processing the        information contained in the image,    -   a processor connected to the storage means, which effects the        instructions of:    -   first processing for defining an area of interest of the image,    -   effecting an adaptive thresholding of the area of interest, in        order to obtain a thresholded image of this area of interest,        referred to as the first thresholded image,    -   segmentation of the thresholded image, in order to obtain a        first set of morphological layers of the thresholded image.

Other instructions can relate to other steps or other embodiments of themethod according to the invention as described above.

The device or system described above uses a program for a computer,resident on a medium which can be read by a computer and containing theinstructions enabling a computer to implement the method of processinginformation contained in an image according to the invention, i.e.:

-   -   effecting a first processing, for defining an area of interest        of the image,    -   effecting an adaptive thresholding of this area of interest in        order to obtain a thresholded image of this area of interest,        referred to as the first thresholded image,    -   segmenting the thresholded image, in order to obtain a first set        of morphological layers of the thresholded image.

Other instructions can be provided, which correspond to variousembodiments or to particular steps of the information processing methodas described above.

The method according to the invention can also be implemented accordingto a “component” or “hard” version.

The skeletonisation method (vectorisation algorithms) supplements thework of B. Taconet (TAC90) described in “Two skeletonisationalgorithms”, Transactions of the RAE Colloquium, Le Havre, BIGRE 68, p.68-76, 1990. One of the advantages of this algorithm is its relativeinsensitivity to noise. In addition, this algorithm is effective interms of processing time. The labelling of the elements as a function oftheir degree of interiority in the line is also a not insignificantasset, whose usefulness will be disclosed a little later. This isbecause the skeleton issuing from this method is in fact a standardisedimage whose pixels are labelled according to their degree of interiorityin the shape. The measurement of the thickness of the lines is thusdirectly accessible by virtue of this labelling.

This algorithm includes a step of seeking the degree of interiority ofeach pixel, and then a step of seeking the pixels with the highestdegree of interiority.

1. Seeking the Degree of Interiority of Each Pixel

The first step consists in labelling the pixels according to theirdegree of interiority in the shape. It is broken down into twosuccessive passes over the image. Each passage corresponds to theapplication of an L-shaped mask which makes it possible to label thepixels according to their degree of interiority. The first L-shaped maskis applied to the image, passing over it from top to bottom. Each pixelP of the object is labelled according to the following mask (first maskfor the construction of the image of the degrees of interiority):

P P1 P4 P3 P2P is then labelled according to its surroundings, in accordance with thefollowing rule: P₀=lower(P1+1, P2+1, P3+1, P4+1).

The second scanning is effected from the bottom to the top of the image.The mask applied is as follows (second mask for the construction of theimage of the degrees of interiority):

P6 P7 P8 P5 PP is then labelled according to its surroundings, in accordance with thefollowing rule: P₁=lower(P5+1, P6+1, P7+1, P8+1).

The degree of interiority adopted for each pixel P is the largest valueof P₀ and P₁ (higher(P₀, P₁)).

Pixels with the highest degree of interiority correspond to the pointson the skeleton. The skeleton is obtained by seeking all these pointswith the maximum label.

2. Formation of the Core Image: Seeking the Pixels with the HighestDegree of Interiority

This second step consists in extracting the significant points of theskeleton, constructing a “core” image from the image of the degrees ofinteriority. The core image is in fact an image where the thickness ofthe lines is at most equal to two. It is calculated using twelve 3×3masks. The twelve masks are obtained by rotating the three masksillustrated below about the central pixel marked P(P=current point,C=contour point, X=point on the background or the internal mass):

XCX XCX XCX CPC XPC XPX XXX CXX CXC

At each iteration, the processed pixels are those whose degree ofinteriority is equal to the order of the current iteration as well asthose, with lower degrees, which were kept during the previous steps.

The refinement of the core image according to the surrounding conditionsexpressed by the 16 masks obtained by rotating the other masks (below)finally makes it possible to obtain the skeleton.

X0X 000 000 X00 1P1 X11 11X 1P0 X1X X11 11X X1X

Since the process of skeletonisation does not afford any structuring ofthe information, a monitoring of the skeleton is then effected whichwill make it possible to effect this structuring.

The shape recognition method according to the invention will beexplained in application to linear objects (a network and its differentsections). It can be implemented on other types of objects.

From the skeleton determined above, a skeleton tracking makes itpossible to construct a graph corresponding to the linear objectsprocessed. The recognition of the network results from thisreconstruction. At the end of this processing, the information isstructured in the form of a graph connecting all the nodes of the image.

In order to effect the reconstruction of the network, a polygonalisationtool is available: such a tool makes it possible to generate segmentsfrom points. An example of such a tool is described in the doctoralthesis of the University of Rouen (1994) by J. M. Ogier entitled“Contribution to the automatic analysis of cartographic documents:interpretation of cadastral data”.

There are also available lists of points with different structures. Thisis because, if there are multiple points on the skeleton, there may bedifferent possibilities of pursuing the processing. Thus two lists oftracking results are formed, and the data are merged in order to obtaina tracking of the line best meeting the topology of the shape to bereconstructed.

There is also available information on the thickness of the originallines: it is in fact a case of the information (the degree ofinteriority) resulting from the algorithm described above.

Finally, there is the image on which the work has been carried out, i.e.here the morphological layer concerned (resulting from the segmentationoperation) and the original image. The morphological layer concernedwill in general be the one corresponding to the elements, or objects,here linear.

In addition, whenever the application or occupation concerned(recognition of a telecommunication network drawing, or of a water orgas distribution network, or of an electrical distribution network),there are rules imposed by the industry itself. In particular thetelecommunication cable lines, or sections, connect the cable chambers,the water pipes connect the water distribution points, the electricallines connect the branch boxes, etc. Generically, the arcs of thenetwork connect the nodes of this network. At the nodes of the networkthere may be “chambers”.

In the field of telecommunications, the model of the France Telecomnetwork teaches that an arterial section is delimited by two, and onlytwo, infrastructure nodes. An infrastructure node may be a chamber, asimple node or a support on a facade. In addition, an unlimited numberof arterial sections may start from an infrastructure node.

In more general terms, the rules of organisation of the data of a theme,of whatever nature, supply information useful to the recognition of adocument. This modelling is related to the industry which led toproducing this document. It is therefore possible to define, in arecognition chain, “industry-oriented modules” which are well delimited,in order to make it possible, on the assumption of a reuse of themethod, to modify only part of the system, and to preserve its genericparts.

The reconstruction of the network makes it possible to fabricate lines,or arcs, from the skeleton (all the pixels of the skeleton).Manufacturing these lines, or arcs, results from the application of theindustry rules to the data of the skeleton.

The reconstruction of objects uses different information primitive, forexample:

-   -   the model of the data, which affords the information related to        the industry,    -   the segmentation primitives (bipoints) which constitute the base        elements of the objects to be known,    -   information issuing from objects recognised in other        morphological layers (the cable “chambers” in the case of        telecommunications).

Polygonalisation makes it possible to produce arcs (in fact bipoints, orsegments).

A polygonalised itinerary corresponding to the example already givenabove (in relation to FIGS. 2 to 7) is given in FIG. 9. All thearticulation (or reversal) points, as well as the start and end pointsof the segments, are represented by spheres.

In this figure, there can be seen, amongst the segments, those whichcorrespond to elements of the network 50, 52 and those which correspondto the edges of the plots 54, 56, 58.

A major part of the elements of the network consist of long linearobjects. The plot edges which have remained do however have notinsignificant lengths.

These linear elements constitute a first primitive on which it ispossible to rely in order to produce an outline of the network,consisting of large segments, and in which the only thing left would beto identify the missing elements. Such missing elements are, forexample, marked in FIG. 9 by the references 60, 62.

A second primitive is based on the thickness of the lines. Itconstitutes a primitive which is broadly discriminating between thenetwork and the plot edges.

For this purpose a threshold is defined. A threshold on the average ofthe degree of interiority in the image does not appear to give a genericresult. At this stage, its definition remains manual.

At the multiple points of the skeleton, there may be differentpossibilities of pursuing this processing. Thus, two lists of trackingresults are produced, and the data are merged in order to obtain a linetracking best meeting the topology of the shape to be reconstructed.

For the reconstruction, two skeleton tracking algorithms are thereforeused, each of them generating a list of objects.

The first (or respectively the second) algorithm effects a linetracking, favouring, in the case of any doubt, a bifurcation to the left(or respectively to the right) in the case of a multiple node.

To pick up the example of FIG. 9, the application of these twoalgorithms results in the trigonometric and anti-trigonometric trackingdepicted in FIGS. 10A and 10B.

There are therefore obtained two lists of segments structuring theinformation so that the network is represented therein in a relativelycontinuous manner. In order to exploit these two lists to the maximumextent and to find the entire continuity of a section between two nodes,the data are merged, which reveals the long straight objects, since theywill each be represented by two relatively distant points.

It is also possible to select these data, i.e. the bipoints, in order tosegment the information present in the two lists.

In addition, if a selection is made on the long objects, there is morechance of keeping only the network elements of the “section” type,excluding part of the plot edges and almost all the barbs. This isbecause the threshold used for effecting the polygonalisation is thethickness of the original line. Thus the plot edges which are finerlines have more chance of being divided up by the polygonalisation.Nevertheless, by this selection, the elements constituting the chambersare also excluded.

The threshold as from which it is possible to use the term “long object”is not precisely fixed. It is possible to use a threshold ofapproximately 120 pixels, which corresponds to a length on the paperdrawing of approximately 0.6 mm (resolution of 400 dpi). The processingsare little sensitive to the fluctuation of this threshold, and thus amanual determination seems suitable. On the few tests carried out, thisthreshold has always appeared suitable, without having to be modified.

The merging of data makes it possible to eliminate the redundancy whichexists in each list issued from the two different trackings, whilstmaking it possible to complete the information on the long objects inone list, with respect to the other. At the end of this processing,there will remain no more than a list reduced to the maximum extent,reconstituting the major part of the section of the network.

One problem posed by this merging is that the segments issuing from thepolygonalisation do not overlap totally. This problem stems from thefact that the starting node and end node are different for the twotrackings. Thus, after polygonalisation, the same paths are not exactlyobtained. They are nevertheless very close since they both belong to themorphological layer of the sections of the network.

It is therefore attempted to sort and connect together these twosegments in order to obtain a description of all the sections of thenetwork which is as complete as possible.

The sorting consists in eliminating, from one list, the bipoints whichare included in the other.

For this purpose, an inclusion criterion is defined which makes itpossible to determine whether a bipoint is close to another.

In order to define the inclusion, two steps are carried out. The firststep (FIG. 11) consists of the delimitation of a region inside which asource bipoint 70 is situated. A region 72 which is the rectangleencompassing the bipoint is then delimited; this can be enlarged inheight and width, for example, by twice the degree of interiority of thesegment from which it came. Thus bipoints 74, 76 are sought which wouldbe included in this region. This is the first inclusion criterion.

The second inclusion criterion consists in measuring, for eachpreviously selected bipoint 74, the distance d which exists with thestraight line formed by the source bipoint 70. The distance calculatedis that of the orthogonal projection of a point on a straight line, forthe two points which make up each bipoint. If this distance is less thana threshold for the two points of a bipoint, it is then considered thatthere is total inclusion. The threshold is for example equal to thedegree of interiority, which makes it possible to obtain very goodresults.

Partial inclusion is also defined, which consists in detecting a singlepoint of a bipoint included in a source bipoint.

The above processings will be effected not by reading on the image, butdirectly from the information issuing from the lists.

From the elements thus defined, the redundant information is eliminated.For this purpose, a source list is arbitrarily chosen with which theinclusion criterion, defined above, can be evaluated with the otherlist.

Any bipoint included completely in a source bipoint can be eliminated.

Following this processing, the role of these two lists is reversed andthe process is reinitiated.

All that remain then are partial inclusions between the bipoints in thetwo lists.

The partially included bipoints can also be merged.

There are various types of possible configurations, two main cases 80,82 of which are depicted in FIG. 12. The reference 80 designates a caseof non-colinear bipoints and the reference 82 a case of colinearbipoints.

According to this figure, it is advantageous to merge two colinearbipoints into a single bipoint.

This is because these two bipoints then express the same section part.This partial redundancy can therefore be eliminated. However, twonon-colinear segments are not merged.

One of the difficulties lies in determining the colinearity. This isbecause it is possible to find two segments very close together, andalmost colinear, without their actually being so.

This difficulty is got round by the use of a polygonalisation algorithmsuch as the one already implemented previously. This is because, if thecoordinates of the two bipoints to be merged are put through thepolygonalisation function, with the degree of interiority as a maximumerror, the result will be a single bipoint if the two starting bipointsare actually colinear and come from the same section.

In order to perform the final merging operation, two distinct lists areno longer worked on, but rather a single list containing the previoustwo lists. This makes it possible to optimise the choice of bipoints.This is because working on two lists amounts to seeking the continuityof a bipoint of one list in the other list, which is not necessarily themost judicious solution.

Thus, following a bipoint, it is possible to find two other candidatebipoints (one on each list). The is best choice is considered to be thelongest bipoint. It therefore suffices to make only one list and toorder it in increasing order of length. In this way, the order oftesting the bipoints will establish the required priority.

Therefore all the bipoints are placed in a single segment (source list),and then they are classified by increasing order of length.

A first bipoint (the largest) is taken and partial inclusion with theother bipoints is sought. When a bipoint partially included in thelargest bipoint is found, the two bipoints are tested bypolygonalisation.

This phase uses the knowledge of the order in which the points follow onfrom each other in order to constitute a single bipoint. This is becausethe polygonalisation algorithm uses the knowledge of the two end pointsof the set of bipoints to be processed. It is therefore sought to knowthe position of the different points with respect to each other:

-   -   if the result of this processing is a bipoint, then the two        bipoints have been correctly merged. In this light, the two        bipoints in the source list are erased. A new partial inclusion        search is then initiated from the new bipoint thus created, and        the cycle recommences,    -   if the result of the polygonalisation includes more than two        points, the alignment of the two bipoints is not in conformity.        The partial inclusion search then continues.

When the partial inclusion search no longer supplies any new bipoints,the bipoint issuing from this processing is placed in a result list(storage in memory). If no modification has been made to this bipointduring the processing, it is erased from the source list.

The largest of the bipoints which remains in the source list is thenused for reinitiating the processing.

Then there remains in the result segment only the bipoints making up theoutline of the processed network. One example of a result, from thelists of FIGS. 10A and 10B, is given in FIG. 13. Discontinuities stillremain in the network. In addition, the chambers have obviouslydisappeared.

The following step is the structuring of the bipoints. For this purposethe bipoints are organised so as to group together those which followeach other in the same segment. All the segments thus constituted willbe placed in a list of segments.

The purpose of this structuring is to collect together, in the samesegment, the contiguous bipoints which belong to the same sections. Thisorganisation will facilitate the analysis of the missing pieces. Inaddition, this structuring is close to that required at the output ofthese processings. This is because a section is a separate entity. Eachsection must therefore preferably be recognised individually, whencethis structuring. In order to effect this grouping, it is possible toproceed by proximity analysis.

Thus physical continuity in the very close vicinity of each point makingup the bipoint to be extended is sought. This search is carried out stepby step, until a possible series is no longer found. All the bipointsthus detected are stored in the same segment. The order in which thebipoints are stored makes it possible to preserve the logicalconcatenation of the points from one end of the section to the other,the ends being the most important points to locate.

Regrouping processings are also carried out directly from the lists,without returning to the image. These processings use the detection ofinclusion as described previously. When a point is detected, the bipointfrom which it came in its entirety is of course considered. The relativeposition of this bipoint is known with respect to the source bipoint.

This is permitted by a classification of each point making up thebipoints. Thus the first element of a bipoint always has a smallerX-axis than its successor. If it is equal, then the classification iseffected on the Y-axis of the points under consideration. This makes itpossible to know the relative position of the four points processed. Theinclination of each bipoint (increasing or decreasing) is also takeninto account. The inclusion function then sends back an indicator whichspecifies the order in which the points must be stored.

The storage order may be disturbed by the presence of a bifurcation.Thus it is no longer one, but two bipoints or more, which may bedetected close to a point. There then no long exists a possible orderwith the structuring used. A structuring in tree form resolves thisproblem.

In FIG. 14, the bipoints grouped together within a segment are delimitedby crosses. The results are satisfactory.

The bipoints obtained after this step come from several processings:skeletonisation and then skeleton tracking, polygonalisation, mergingand sometimes even polygonalisation once again. Even if theparameterising of the processings preserves the validity of the results,verification may be useful.

This verification is based on a return to the morphological layer of thesections.

The equations of the straight lines passing through each bipoint arecalculated. Then, by means of these equations, the layer or initialimage is run through, noting the label (the degree of interiority) ofeach point situated between the two ends of a bipoint. This makes itpossible to establish a label percentage on the path, and therefore toverify the percentage to which the bipoint belongs to the layer.

Consequently, it is verified whether each of the points between two endsof a bipoint has a non-zero degree of interiority in the initial image,and therefore belongs to a shape in the image or to a morphologicallayer.

This processing is carried out as an indication, but could make itpossible to call into question a bipoint for a possible recentring onthe layer.

It is difficult to draw conclusions on the difference between a 90%validity and a 100% validity. Since the quality of the layer is notperfect, points on a section which do not have a label are thusfrequently found. An expansion of this layer resolves this problem.Nevertheless, a validity of less than 80% may seem suspect.

The values encountered up to now are very often up to 100% and in anyevent greater than 90%.

The principle of the location of the chambers is still to be explained.The starting point is the principle that it is unnecessary to seek toreconstitute the chambers with the information coming from thevectorisation. This is because the skeletonisation and the processingswhich precede it have made this information fairly inconsistent with theinitial representation. The representation on the original drawings isalso sometimes itself distorted. Thus a chamber is normally representedby a rectangle. The small size of these elements on a drawing seems tobe the cause of these often ovoid representations, as can be seen inFIG. 15, where a chamber is designated by the reference 90.

A first location of the chambers is effected by seeking occlusions onthe morphological layer of the sections. This operation is performed bydetecting closely related masses. For each occlusion, the coordinates ofthe enclosing rectangle and the lower perimeter are supplied. The shape16 represents various possible types of occlusions: some (92) representa chamber, and others (94, 96, 98, 10) do not.

In order to effect a first filtering on the occlusions, two primitivesof the same type as those which made it possible to separate thedrawings in accordance with three layers were determined. They are basedon the internal perimeter of the occlusions. It has thus been possibleto determine, on drawing samples, a minimum perimeter of thirty pixelsand a maximum perimeter of one hundred and fifty pixels, for aresolution of 400 dpi. Scaling these parameters may be effected by meansof a simple proportionality rule.

This first filtering does not suffice to select solely the occlusionsissuing from a chamber. A third primitive is therefore used forvalidating the hypothesis of a chamber. This third primitive is based onthe number of segment ends which are situated in the vicinity where thechamber was detected. Thus, in the very great majority of cases, achamber is connected to at least two sections. The segment ends (andtherefore the sections) are sought, for example, in a square whosecentre is the centre of the detected occlusion, and with a side equalfor example to 30 pixels. This threshold comes from the experimentation.It functions correctly with the drawings available. An excessively largethreshold may cause a false detection. An excessively small thresholdmay cause non-detection. Scaling this threshold is done byproportionality according to the scale of the drawing to be processed.

Validation of the above three primitives gives rise to validation of thedetection of the chamber.

The chamber is then represented by a standardised square with a 10 pixelside. Nevertheless, the original coordinates of the chambers will bestored in a vector containing the coordinates of the top left hand pointof the rectangle circumscribing the occlusion, and will be the lengthand height of this rectangle. This approach enables the chambers to becorrectly located and recognised.

The shape recognition method or algorithm described may be used by meansof a device as already described above, in relation to FIG. 8. Theprogram instructions corresponding to the shape recognition methoddescribed above can be stored instead of, or as a complement to, theinstructions for the information processing method contained in an imagedescribed at the start of the present description.

A device for implementing a shape recognition method according to theinvention therefore has:

-   -   storage means, for storing image information,    -   a processor, connected to the storage means, which effects the        instructions of:    -   skeletonisation of the image, in order to establish a skeleton        of the image,    -   polygonalisation using the pixels of the skeleton of the image,        in order to generate bipoint segments,    -   structuring of the bipoints in order to collect together those        belonging to the same shape of the image.

Other instructions can be effected by the processor, which correspond toparticular embodiments of the shape recognition method according to theinvention as described above.

A device or system according to the invention uses a program for acomputer resident on a support medium which can be read by a computerand which contains instructions enabling the computer to implement theshape recognition method according to the invention, and in particularthe three steps which have just been stated above. It may also containother instructions for performing other steps of the shape recognitionmethod as described in the present application.

1. A method of processing information contained in an image (4), including: a first processing (1-4) for defining an area of interest of the image (8), said area of interest being computed from a frequency distribution of the image, effecting an adaptive thresholding (1-8) of the area of interest in order to obtain a thresholded image (10) of the area of interest, referred to as the first thresholded image, segmentation (1-12) of the thresholded image, in order to obtain a first set of morphological layers (14-1, 14-2, 14-3 . . . ) of the thresholded image, shape recognition processing applied to each of the morphological layers of the first set of morphological layers, the shape recognition processing applied including: a skeletonisation of each morphological layer, in order to establish a skeleton of elements of this layer, a polygonalisation using the pixels of the skeleton of this layer, in order to generate segments or bipoints, a processing for determining the shapes to be recognised at the level of the multiple points, following the polygonalisation, the processing for determining the shapes to be recognised at the level of the multiple points including the use of first and second skeleton-tracking algorithms: the first algorithm effecting a line tracking favoring a bifurcation to the left in the case of a multiple node, generating a first skeleton tracking, the second algorithm effecting a line tracking favoring a bifurcation to the right in the case of a multiple node, generating a second skeleton tracking, a structuring of the bipoints, in order to collect together those belonging to one and the same share of the morphological layer, merging the data resulting from the application of two skeleton-tracking algorithms, in order to eliminate the redundant information contained in the two skeleton trackings, the merging of the data including the determination of the segments, or bipoints, of one of the skeleton trackings, which are included, partially or totally, in the other, and performing the steps of: a) the establishment of a single list of bipoints, in increasing order of length, b) the selection of the largest of the bipoints in this last list, c) seeking of partial inclusion with the other bipoints, d) when a partially included bipoint is found during the previous step, testing the two bipoints by polygonalisation, e) if the result of step d) is positive, the erasure of the bipoints, replacement by the merged bipoint, and return to step c), f) the continuation of step c), if the result of step d) has more than two points, g) if step d) supplies no new bipoints, the storage of the last bipoint issuing from step d), the erasure of this bipoint from the list of bipoints established at step a), and return to step a).
 2. A method according to claim 1, the first processing (14) making it possible to define an area of interest of the image, being effected by thresholding or multithresholding.
 3. A method according to claim 2, the thresholding or multithresholding steps using the OTSU or KITTLER-ILLINGWORTH algorithm.
 4. A method according to claim 1, the first processing being followed by a step of refining the area of interest of the image.
 5. A method according to claim 4, the refinement step being performed by expansion or erosion of the area of interest defined in the image.
 6. A method according to claim 1, including, after segmentation of the thresholded image, performance of a step of thresholding the parts of the thresholded image corresponding to one of the morphological layers, the image obtained being referred to as the second thresholded image (16-1, 16-2, 16-3, . . . ).
 7. A method according to claim 6, the second thresholded image being segmented, in order to obtain a second set of morphological layers.
 8. A method according to claim 7, in which a shape recognition processing is applied to each of the layers of the second set of morphological layers.
 9. A method according to claim 1, each morphological layer connecting together closely related masses of pixels of the image.
 10. A method according to claim 1, the image being a technical drawing.
 11. A method according to claim 1, the skeletonisation including: a search for the degree of interiority of each pixel, a search for the pixels with the highest degree of interiority.
 12. A method according to claim 1, the total (or partial) inclusion of a bipoint, referred to as the bipoint to be tested, in a bipoint, referred to as the source bipoint, being determined as a function of the following criteria: the presence of the bipoint to be tested (74, 76), or of one of its ends, in a predetermined region (72) around the source bipoint (70), a distance, between the bipoint to be tested (74, 76) and the source bipoint (70), less than a certain threshold.
 13. A method according to claim 12, the threshold being equal to the degree of interiority of the source bipoint.
 14. A method according to claim 1, also including a step of eliminating the bipoints of one of the skeleton trackings, which are completely included in the other, and vice-versa.
 15. A method according to claim 1, also including a step of merging the bipoints (80, 82) of each of the two lists which are partially included in the other list.
 16. A method according to claim 15, including the merging of the colinear bipoints (82) into a single bipoint.
 17. A method according to claim 16, the colinearity of the two bipoints being determined by applying a polygonalisation algorithm to these two bipoints, the degree of interiority of one of the two bipoints being taken as a margin of error.
 18. A method according to claim 1, also including a step for collecting together the contiguous bipoints in one and the same segment.
 19. A method according to claim 18, the collecting together of the contiguous bipoints being effected by seeking, step by step, physical continuity in the very close vicinity of each point of a bipoint to be extended by contiguity.
 20. A method according to one of claims 18 or 19, the collecting together of the contiguous bipoints in the same segment using an inclusion algorithm.
 21. A method according to claim 18, the bipoints being structured in the form of trees at the bifurcation points.
 22. A method according to claim 1, also including a verification step consisting of verifying whether each point in the bipoint is contained in one of the elements in the layer.
 23. A method according to claim 1, the layer subjected to the shape recognition method having chambers situated at ends of sections, this method also including a step of seeking occlusions (92, 94, 96, 98, 100) in the layer, a step of filtering the occlusions, and a step of seeking the number of section ends situated in the vicinity where a chamber (90) was detected.
 24. A method according to claim 23, the occlusions being sought by the detection of closely related masses.
 25. A method according to one of claims 23 or 24, the filtering being effected as a function of the internal perimeter of each occlusion.
 26. A device for performing a method of processing information contained in an image, the device comprising: first processing means for defining an area of interest of the image, said area of interest being computed from a frequency distribution of the image; thresholding means for effecting an adaptive thresholding of the area of interest in order to obtain a thresholded image of the area of interest in order to obtain a threshold image of the area of interest, referred to as the first thresholded image; segmentation means for segmentation of the thresholded image in order to obtain a first set of morphological layers of the thresholded image; shape recognition means for applying a shape recognition method or processing, the shape recognition means comprising: skeletonisation means for effecting a skeletonisation of each morphological layer, in order to establish a skeleton of elements of this layer, polygonalisation means for effecting a polygonalisation from the pixels of the skeleton of this layer, and structuring means for structuring the bipoints and collecting together those belonging to one and the same shape in this layer: second processing means for determining the shapes to be recognised at the level of the multiple points, following the polygonalisation, the processing for determining the shapes to be recognised at the level of the multiple points including the use of first and second skeleton-tracking algorithms, the first algorithm effecting a line tracking favoring a bifurcation to the left in the case of a multiple node, generating a first skeleton tracking, and the second algorithm effecting a line tracking favoring a bifurcation to the right in the case of a multiple node, generating a second skeleton tracking; merging means for merging the data resulting from the application of two skeleton-tracking algorithms, in order to eliminate the redundant information contained in the two skeleton trackings, the merging of the data including the determination of the segments, or bipoints, of one of the skeleton trackings, which are included, partially or totally, in the other, and means for performing the steps of: a) the establishment of a single list of bipoints, in increasing order of length, b) the selection of the largest of the bipoints in this last list, c) seeking of partial inclusion with the other bipoints, d) when a partially included bipoint is found during the previous step, testing the two steps by polygonalisation, e) if the result of step d) is positive, the erasure of the bipoints, replacement by the merged bipoint, and return to step c), f) the continuation of step c), if the result of step d) has more than two points, g) if step d) supplies no new bipoints, the storage of the last bipoint issuing from step d) the erasure of this bipoint from the list of bipoints established at step a), and return to step a).
 27. A device according to claim 26, having means for executing first and second skeleton-tracking algorithms: the first algorithm effecting a line tracking favoring a bifurcation to the left in the case of a multiple node, generating a first skeleton tracking, the second algorithm effecting a line tracking favoring a bifurcation to the right in the case of a multiple node, generating a second skeleton tracking.
 28. A device according to claim 27, the means for executing first and second skeleton-tracking algorithms also making it possible to merge the data resulting from the execution of the two skeleton-tracking algorithms, in order to eliminate the redundant information contained in the two skeleton trackings.
 29. A device according to one of claims 26 or 27, having means for collecting together the contiguous bipoints in one and the same segment. 